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COMPUTER SCIENCE DEPARTMENT Technion - Israel Institute of Technology July 8, 2012 Industrial Project (234313) Tube Lifetime Predictive Algorithm Students: Nidal Hurani, Ghassan Ibrahim Supervisor: Shai Rozenrauch Goals Finding tube lifetime predictive algorithm based on parameters and results of the CT Radar system The algorithm target is to predict with a precision of 75% the lifetime of the tubes Algorithm implementation Obstacles Raw data was not reliable Completing the missing data in order to use it correctly Finding parameters and measures which influence the most of the lifetime of the tube Fit to a known statistical model which can describe the tube lifetime given these parameters Dealing with huge data Methodology Run queries over the database (SQL) to retrieve the relevant data set Processing and transforming the data into a training set which is used later in the predictive algorithm Building a windows form application which can “talk “ with R Fitting a decision tree using CART ( Classification and Regression Tree) for the giving training set Predict a tube lifetime given a vector of estimated parameters or measures Environments &Technologies Main programming language - C# IDE - Visual studio 2010 Statistical tool JMP 7 - for finding possible statistical models which can describe the problem EXCEL (MS office) R (Statistical Language) RCOM MSSQL JMP 7 Achievements A predictor with ±120 days error in general 76.8293% of the predictions with ±60 days error User friendly program Conclusions The more the training set reflect the tube real behavior the more accurate the algorithm shall predict Depends for example on the way of completing the data & also the amount of data needs to be complete Having a comprehensive training set gives more accurate results The algorithm somehow is “flexible” Whenever a new parameter is recognized as a huge influencer